package com.imsl.test.example.stat;

import com.imsl.math.Quadrature;
import com.imsl.stat.EGARCH;
import com.imsl.stat.ExtendedGARCH;
import com.imsl.stat.Pdf;
import com.imsl.stat.TimeSeries;
import java.util.logging.Level;
import java.util.logging.Logger;


/**
 * 
 * <p>
 * Fits an EGARCH(1, 1) with a user defined distribution on \(z_t\).
 * </p>
 * 
 * <p>
 * This example fits the exponential GARCH (EGARCH) to a segment of S&amp;P 500 
 * returns for a period in 1988.  Forecasts 4 steps ahead from the end of the series
 * are generated.  A t-distribution is defined for \(z_t\), along with estimation 
 * of \(E[|z_t|]\) using a quadrature method.
 * </p>
 *
 * @see <a href="EGARCHEx2.java">Code</a>
 * @see <a href="doc-files/EGARCHEx2.out">Output</a>
 */
public class EGARCHEx2 {
   
     public static void main(String args[]) {

        double data1[] = {
            0.940543272, 2.664279727, 0.252045652, 1.221098464, 0.270575973,
            0.446882726, 0.073672969, -4.450030305, 0.007699415, -0.215808015,
            -0.498908466, -0.696433123, 0.113159704, 1.440322251, 0.887874183,
            0.5585227, -0.049267619, -0.452119841, -0.488606538, 0.087972619,
            0.549032948, -1.024238871, -0.589472147, -0.507487482, -0.365744931,
            0.42010331, -1.687159586, 0.212950629, 1.147614577, 0.748805609,
            -1.311175287, -1.574960025, 0.484204788, 0.178009897, -0.869311816,
            1.062785115, 0.098566846, 0.342257263, -0.47633197
        };

        TimeSeries ts;
        ts = new TimeSeries();
        ts.setSeriesValues(data1);

        class myZdistribution implements ExtendedGARCH.zDistribution {
            private double df = ts.getLength() - 4;
             class QuadratureFunction implements Quadrature.Function {
                @Override
                public double f(double z) {
                    return StrictMath.abs(z) * zPdf(z);
                }
            }

            @Override
            public double zPdf(double z) {
                double pdf = 0.0;
                try {
                    pdf = Pdf.noncentralStudentsT(z, df, 0.0);
                } catch (Pdf.AltSeriesAccuracyLossException ex) {
                    Logger.getLogger(EGARCHEx2.class.getName()).log(Level.SEVERE, null, ex);
                }
                return pdf;
            }
          
            public double getExpectedAbsZ() {
                Quadrature q = new Quadrature();
                QuadratureFunction fcn = new QuadratureFunction();
                double a = -5;
                double b = 5;
                q.setRule(2);
                return  q.eval(fcn, a, b);
            }
        }

        myZdistribution myZdist = new myZdistribution();
        
        EGARCH egarch = new EGARCH(ts);
        egarch.setZDistribution(myZdist);
        double[] values=new double[1];
        values[0]=myZdist.getExpectedAbsZ();
        egarch.setValues(values);

        String[] names={"omega","alpha","beta","gamma"};
        try {
            egarch.estimate();
            double[] parameters = egarch.getParameters();
            int nParameters = egarch.getNumberOfParameters();
            int T = egarch.getT();
            double[][][] forecasts = egarch.forecast(1, 4);
            
            System.out.println("Parameter estimates:");
            for(int j=0;j<nParameters;j++){
              System.out.printf("%s:  %3.4f \n", names[j], parameters[j]) ;
            } 
            System.out.println("\n Forecasts 1-4 steps ahead from T:");
            System.out.println("t \t Epsilon \t Conditional variance ");
            for(int j=0;j<4;j++){
              System.out.printf("%d \t   %3.4f         \t%3.4f\n", T+j+1, 
                      forecasts[0][j][0], forecasts[0][j][1]);
            }
        } catch (Exception ex) {
            Logger.getLogger(EGARCHEx2.class.getName()).log(Level.SEVERE, null, ex);
        }
    }
 
}